Rank One Computing was founded in 2015 with a mission to deliver top performing face recognition and computer vision algorithms with a no-nonsense business approach and deep commitment to best practices in software engineering and pattern recognition algorithm design.

Over the course of the last seven years Rank One has raised the industry-wide bar on what is possible with a software development kit. The ROC SDK has demonstrated that top-tier face recognition accuracy can be delivered alongside algorithm efficiency metrics that are often 10x better than industry peers. It has further demonstrated that API’s for an SDK can be lightweight but highly effective and easy to integrate. Rank One’s technical support has consistently separated us from other solutions, because our lead engineers provide rapid responses to technical inquiries. ROC SDK gives trusted biometrics a new meaning since Rank One is an employee-owned company that develops its software entirely in-house and in the U.S.A. Finally, Rank One has led the way by establishing the facial recognition industry’s first code of ethics to govern the use of our algorithms and software in both commercial and government applications.

Building on this strong foundation, Rank One announces a new chapter in its algorithmic offerings with the release of the ROC SDK version 2.0.

ROC SDK version 2.0 delivers both substantial face recognition improvements and a completely new feature set of computer vision and machine learning capabilities. Specifically, the SDK now supports detection of a wide range of objects and text recognition on a wide variety of challenging images (e.g., license plates in the wild).

Introducing ROC SDK 2.0
The significant improvements delivered with ROC 2.0 include:

Face Recognition Algorithms
The following analysis is based on the National Institute of Standards and Technology (NIST) Face Recognition Vendor Test (FRVT) report released on January 20th, 2022. All statistics provided are directly as listed in the report, which is generally from Tables 7 to 24.
Algorithm Improvements
On the heels of v1.26 that delivered substantial accuracy improvements just five months ago, ROC SDK Version 2.0 delivers some of the largest face recognition accuracy improvements ever to the ROC SDK.. All together ROC has cut error rates in half over the last four months as demonstrated by the following reduction in error rates in the NIST FRVT Ongoing benchmark:
Accuracy Compared to Industry Peers
With these substantial error rate reductions, ROC SDK v2.0 – the entirely U.S. owned and developed facial recognition SDK – has basically reached performance parity with the highest ranked FRVT performers which are all currently of Chinese origin:
ROC 2.0 consistently ranks as one of the top-performing algorithms across a wide range of datasets and operating conditions measured by NIST FRVT. The chart below further demonstrates the top-tier accuracy delivered by ROC 2.0 when compared against the median performing algorithms. On the y-axis is False Non-Match Rate, shown on a scale of 0% to 4%, whichideally is minimal (lower is better):
Algorithmic Efficiency
The ROC SDK is the effective industry leader in algorithm efficiency while also providing top-tier accuracy. Truly no other vendor can claim this distinction. As compared to the median FRVT algorithm, the ROC SDK is substantially more efficient across the board:
Other industry algorithms, particularly high accuracy algorithms, require substantial hardware resources compared to the ROC SDK. The implication of top-tier accuracy alongside top-tier efficiency is that integrators and customers can save substantial amounts of money on hardware costs. In certain cases, whether it is a high quantity of on-edge processors, or a centralized processing environment, the savings in hardware costs when using the ROC SDK may cover the cost of licensing the SDK. Thus, taking into account the hardware requirements of industry peers, the ROC SDK can effectively become free. This hardware processing cost advantage scales not only to on-premise deployments but also to cloud deployments on virtual servers or serverless environments which typically charge by CPU hours, RAM, and storage.
Algorithmic Bias
In terms of algorithmic bias, Rank One continues to exhibit minimal differences in error rate between racial and gender cohorts:
The above chart shows the performance variation among white, black, female and male sub-demographics and specifically, the tradeoff between false positive and false negative error rates for each sub-demographic. While the error rates are exceptionally low across all four race / gender cohorts, the lowest overall error rate is with the Black Male cohort (the red, dotted line), particularly at the operational thresholds of 10-4 (1 false match in 10,000 comparisons) and 10-5 (1 false match in 100,000 comparisons). In general the Black Female cohort is the second lowest error rate of all four cohorts (the red, solid line). Such exceptional performance across racial cohorts is counter to the misinformation campaigns that have been propagated by prominent media outlets. But, as continues to be exhibited: top-tier face recognition algorithms are highly accurate on all races.
Summary of Current ROC 2.0 Performance Metrics
The following table provides the summary of ROC’s FRVT performance alongside the median algorithm performance for reference:
Long Term Improvements
Finally, it is important to recognize the orders of magnitude with which the ROC SDK has improved over the past several years. The following plot provided by NIST shows this error rate reduction:
The y-axis is plotted on a logarithmic scale because the improvements are so substantial. Indeed over the course of a few years error rates have been reduced 50x!

These improvements are stunning given how successful the algorithms were in years past. And while such improvements are often being delivered by industry providers, Rank One continues to stand out with the practice of Evergreen Licensing, which offers a simple approach for ROC customers and partners to receive continued access to such ground breaking accuracy improvements.

While the recent improvements to the ROC SDK have reaffirmed its status as the industry leader in top-tier accuracy and efficiency, the innovation train continues to move at a rapid pace via Rank One’s vigorous R&D initiatives. In Spring 2022, we expect to release a new ROC SDK version with yet another powerful set of improvements to the depth of our FR capabilities and breadth of our computer vision capabilities.

Facial Analytics
While Rank One is typically recognized for its excellence in automated face recognition, there are many facial analytics tasks that differ from face recognition itself, but at the same time are highly complementary. The ROC SDK provides some of the best facial analytic tools available, including facial liveness (presentation attack detection), ICAO compliance checks, demographic estimation, occlusion detection, encrypted matching, and facial quality estimation.

With the release of ROC 2.0, substantial improvements are being delivered to this portfolio of facial analytics.

Liveness validation is an automated check to ensure that an image is an actual live presentation of the subject, as opposed to a photograph of that person being held up to the camera (presentation attack). Rank One has a patented micro-texture approach to liveness validation that has been used operationally by ROC customers for several years. And, with ROC SDK 2.0 a new liveness algorithm is being delivered that has significant accuracy improvements. Rank One will deliver more liveness algorithm improvements in 2022, and is in the process of obtaining various certifications for these algorithms, including certification from iBeta for biometric presentation attack detection (PAD) per ISO/IEC 30107-3. 
ICAO Compliance
As passenger travel increasingly relies on face recognition to perform identity verification, a wide range of laws and regulations require that facial photographs of passengers are captured in a manner that complies with the ICAO Portrait Quality for Machine Readable Travel Documents standard. For example, in the European Union all airline passengers must provide an ICAO compliant facial scan prior to boarding a flight in accordance with the Schengen Area Entry/Exit System.

Beyond travel applications, capturing a high quality enrollment photo is a prerequisite for digital identity solutions, access control solutions, and broadly any use case with a database of authorized users. The ROC SDK version 2.0 automated ICAO/ANSI-NIST quality metrics can enable any use case to automatically capture a compliant image from a streaming video source, and thereby ensure high quality, easier-to-match photos in the database of authorized users. This quality thresholding will reduce both false positive and false negative errors in operation by enhancing the quality of probes and database images.

In addition to the ICAO standard, the ISO/IEC 29794-5 Face Image Quality standard is being updated and will become part of a new evaluation report provided by NIST FRVT.

To support these growing requirements for automated face image compliance checks, the ROC SDK now includes a complete automated ICAO compliance check. Specifically, the following factors can be measured and validated by ROC SDK algorithms:

Lighting Portraits shall have adequate and uniform illumination. Lighting shall be equally distributed on the face
Dynamic range The dynamic range of the image should have at least 50% of intensity variation in the facial region of the image.
Pose Head aligned toward the camera (Pitch/Yaw/Roll)
Expression The face shall have a neutral expression
Accessories: Glasses Tinted glasses, sunglasses, and glasses with polarization filters shall not be worn.
Accessories: Head coverings The region of the face, from the crown to the base of the chin, and from ear-to-ear, shall be clearly visible.
Portrait Dimensions + Head Location The head shall be centered in the final portrait
Portrait Dimensions and Head Location The image width A to image height B aspect ratio should be between 74% and 80%
Children Detect child age
Contrast For each patch of skin on the person’s face, the gradations in textures shall be clearly visible
Background Detect whether or not the background is uniform
Pose Eyes aligned toward the camera
Eye visibility Both eyes shall be opened naturally, but not forced wide-opened.
Accessories: Glasses Any lighting artifacts [e.g. glare] present on the region of the glasses shall not obscure eye details
Accessories: Facial Ornamentation Facial ornamentation which obscures the face shall not be present
Style: Makeup, Hair Style The hair of the subject shall not cover any part of the eyes
Rank One will continue to add to this range of compliance checks as new standards emerge.

Further, while these compliance checks can be integrated into existing or new applications via the ROC SDK, the ROC LiveScan application also provides a turn-key solution to performing image compliance checks and extracting the highest quality facial image of a person presenting themselves to a camera. This application is fully customizable to allow for additional ROC analytics to be included as part of the compliance check.

Video Analytics
Advancements in computer vision and machine learning technology have enabled a wide range of new possibilities in the automated analysis of images and videos. One of the most important emerging applications is the use of video analytics to support smart city initiatives, facility security, public safety, and traffic control.

With ROC 2.0 a wide range of new capabilities for video analytics are being delivered. These include object detectors, license plate recognition (LPR), and optical character recognition (OCR) capabilities.

Object Detection
The object detectors deployed with ROC 2.0 enable detecting the following objects:

  • Car
  • Truck
  • Bus
  • Motorcycle
  • Bicycle
  • Person
  • License Plate
  • Gun
  • Military Vehicle
  • Airplane
  • Boat

These detectors offer ROC integrators significant accuracy and efficiency improvements over other off-the-shelf solutions. While there is not an equivalent to FRVT for object detection, there are other methods to establish the effectiveness of the ROC object detectors. For example, when compared to the Yolo v5, a leading open source object detection framework in terms of both accuracy and efficiency, the new ROC detectors offer substantial performance improvements:

The detection rates presented above were measured using 1,000 images for each object detector from the OpenImages dataset. In addition to significantly accuracy improvements as compared to a leading open source solution, the ROC offering is also orders of magnitude faster:
With the ROC algorithm performing over 100x faster than leading open source alternatives while also delivering significant accuracy improvements, it is increasingly clear that ROC’s trade secret methods for delivering top-tier accuracy and efficiency will not just be limited to face recognition and will also set the industry standard for object detection and recognition.
Optical Character Recognition
In addition to object detection algorithms, the ROC SDK now includes the ability to perform Optical Character Recognition (OCR) across a wide range of challenging imagery. The solution is being used to power license plate readers, document processing, and a wide range of OCR-in-the-wild problems.

Stay tuned for more significant algorithm improvements ahead as Rank One Computing has officially entered the ROC 2.0 phase!

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